Saltar al contenido principal
LibreTexts Español

19.3: Simulación de potencia estadística

  • Page ID
    150713
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    Simulemos esto para ver si el análisis de potencia realmente da la respuesta correcta. Tomaremos muestras de datos para dos grupos, con una diferencia de 0.5 desviaciones estándar entre sus distribuciones subyacentes, y veremos con qué frecuencia rechazamos la hipótesis nula.

    nRuns <- 5000
    effectSize <- 0.5
    # perform power analysis to get sample size
    pwr.result <- pwr.t.test(d=effectSize, power=.8)
    # round up from estimated sample size
    sampleSize <- ceiling(pwr.result$n)
    
    # create a function that will generate samples and test for
    # a difference between groups using a two-sample t-test
    
    get_t_result <- function(sampleSize, effectSize){
      # take sample for the first group from N(0, 1)
      group1 <- rnorm(sampleSize)
      group2 <- rnorm(sampleSize, mean=effectSize)
      ttest.result <- t.test(group1, group2)
      return(tibble(pvalue=ttest.result$p.value))
    }
    
    index_df <- tibble(id=seq(nRuns)) %>%
      group_by(id)
    
    power_sim_results <- index_df %>%
      do(get_t_result(sampleSize, effectSize))
    
    p_reject <-
      power_sim_results %>%
      ungroup() %>%
      summarize(pvalue = mean(pvalue<.05)) %>%
      pull()
    
    p_reject
    ## [1] 0.8

    Esto debería devolver un número muy cercano a 0.8.


    This page titled 19.3: Simulación de potencia estadística is shared under a not declared license and was authored, remixed, and/or curated by Russell A. Poldrack via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.